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---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: AmsterdamDocClassificationMistral200T2Epochs
results: []
datasets:
- FemkeBakker/AmsterdamBalancedFirst200Tokens
language:
- nl
---
# AmsterdamDocClassificationMistral200T2Epochs
As part of the Assessing Large Language Models for Document Classification project by the Municipality of Amsterdam, we fine-tune Mistral, Llama, and GEITje for document classification.
The fine-tuning is performed using the [AmsterdamBalancedFirst200Tokens](https://huggingface.co/datasets/FemkeBakker/AmsterdamBalancedFirst200Tokens) dataset, which consists of documents truncated to the first 200 tokens.
In our research, we evaluate the fine-tuning of these LLMs across one, two, and three epochs.
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) and has been fine-tuned for two epochs.
It achieves the following results on the evaluation set:
- Loss: 0.6601
## Training and evaluation data
- The training data consists of 9900 documents and their labels formatted into conversations.
- The evaluation data consists of 1100 documents and their labels formatted into conversations.
## Training procedure
See the [GitHub](https://github.com/Amsterdam-Internships/document-classification-using-large-language-models) for specifics about the training and the code.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9863 | 0.1988 | 123 | 0.8790 |
| 0.7918 | 0.3976 | 246 | 0.8324 |
| 0.5133 | 0.5964 | 369 | 0.7915 |
| 0.5702 | 0.7952 | 492 | 0.7591 |
| 0.7897 | 0.9939 | 615 | 0.6976 |
| 0.5872 | 1.1927 | 738 | 0.6768 |
| 0.4242 | 1.3915 | 861 | 0.6649 |
| 0.5222 | 1.5903 | 984 | 0.6609 |
| 0.2609 | 1.7891 | 1107 | 0.6599 |
| 0.4834 | 1.9879 | 1230 | 0.6601 |
Training time: it took in total 1 hour and 22 minutes to fine-tune the model for two epochs.
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
### Acknowledgements
This model was trained as part of [insert thesis info] in collaboration with Amsterdam Intelligence for the City of Amsterdam.
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